| 1 | from ..base.experiment_niching_abc import ExperimentNiching |
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| 2 | from ..structures.individual import Individual |
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| 3 | from ..structures.population import PopulationStructures |
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| 4 | from ..utils import merge_two_parsers |
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| 5 | from .experiment_frams import ExperimentFrams |
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| 6 | |
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| 7 | |
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| 8 | class ExperimentFramsNiching(ExperimentFrams, ExperimentNiching): |
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| 9 | def __init__(self, frams_lib, optimization_criteria, hof_size, popsize, constraints, normalize, dissim, fit, genformat, archive_size, save_only_best, knn_niching, knn_nslc, crowding_dissim=None) -> None: |
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| 10 | ExperimentFrams.__init__(self, hof_size=hof_size, |
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| 11 | popsize=popsize, |
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| 12 | frams_lib=frams_lib, |
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| 13 | constraints=constraints, |
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| 14 | optimization_criteria=optimization_criteria, |
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| 15 | genformat=genformat, |
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| 16 | save_only_best=save_only_best |
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| 17 | ) |
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| 18 | ExperimentNiching.__init__(self, hof_size=hof_size, |
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| 19 | popsize=popsize, |
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| 20 | fit=fit, |
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| 21 | normalize=normalize, |
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| 22 | save_only_best=save_only_best, |
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| 23 | archive_size=archive_size, |
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| 24 | knn_niching=knn_niching, |
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| 25 | knn_nslc=knn_nslc, |
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| 26 | crowding_dissim=crowding_dissim |
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| 27 | ) |
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| 28 | self.dissim = dissim |
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| 29 | |
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| 30 | |
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| 31 | def initialize_evolution(self, genformat, initialgenotype): |
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| 32 | self.current_generation = 0 |
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| 33 | self.time_elapsed = 0 |
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| 34 | self.stats = [] # stores the best individuals, one from each generation |
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| 35 | initial_individual = Individual() |
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| 36 | initial_individual.set_and_evaluate(self.frams_getsimplest( |
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| 37 | '1' if genformat is None else genformat, initialgenotype), self.evaluate) |
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| 38 | self.hof.add(initial_individual) |
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| 39 | self.stats.append( |
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| 40 | initial_individual.rawfitness if self.save_only_best else initial_individual) |
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| 41 | self.population_structures = PopulationStructures( |
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| 42 | initial_individual=initial_individual, archive_size=self.archive_size, popsize=self.popsize) |
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| 43 | if self.fit == "nsga2": |
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| 44 | self.do_nsga2_dissim(self.population_structures.population) |
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| 45 | if self.fit == "nslc": |
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| 46 | self.do_nslc_dissim(self.population_structures, self.population_structures.population) |
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| 47 | |
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| 48 | def dissimilarity(self, population): |
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| 49 | return self.frams_lib.dissimilarity([i.genotype for i in population], self.dissim) |
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| 50 | |
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| 51 | def crowding_distance_dissimilarity(self, population): |
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| 52 | return self.frams_lib.dissimilarity([i.genotype for i in population], self.crowding_dissim) |
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| 53 | |
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| 54 | |
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| 55 | @staticmethod |
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| 56 | def get_args_for_parser(): |
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| 57 | p1 = ExperimentFrams.get_args_for_parser() |
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| 58 | p2 = ExperimentNiching.get_args_for_parser() |
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| 59 | return merge_two_parsers(p1, p2) |
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| 60 | |
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